What do you think?
Rate this book
No Bullshit Math for Data Science: Your Comprehensive, Short and Sharp Guide to the Mathematics of Data Science
The table of contents and a free sample chapter of available for viewing at
Data science is an interdisciplinary, growing field that uses scientific methods, processes, and algorithms to extract or extrapolate knowledge, patterns, and insights from various kinds of structured, semi-structured, and unstructured data for various real-life and theoretical purposes.
To become a successful data scientist, one must have a solid foundation in mathematics, programming skills, and domain expertise. A data scientist should be able to work with large datasets, manipulate and analyze data using statistical techniques, and communicate the findings effectively to stakeholders. Furthermore, a data scientist should be able to design and develop predictive models using machine learning algorithms to solve real-world problems.
As someone who has been engaging with math, in one form or another, for over a decade, I find it disappointing that there aren't many books that teach the mathematics underneath data science in a comprehensive, short and sharp manner. This book aims at doing exactly that. Really, the need for writing this book is best captured by its title! There is a myriad of books on data science, machine learning and their underlying mathematics, but they're each around 350 pages long, with a high price, which only touch upon a few topics, mainly written in code, with much less actual teaching of mathematics, or doing some but in very advanced levels.
The book has two parts. Part I explores the core mathematics at use in data science. Part II builds upon the knowledge gained in the first part and explores the underlying mathematics of some of the well-known machine learning algorithms and models; it also provides sample codes to implement each of the algorithms in the programming language Python.
Data science is an interdisciplinary, growing field that uses scientific methods, processes, and algorithms to extract or extrapolate knowledge, patterns, and insights from various kinds of structured, semi-structured, and unstructured data for various real-life and theoretical purposes.
To become a successful data scientist, one must have a solid foundation in mathematics, programming skills, and domain expertise. A data scientist should be able to work with large datasets, manipulate and analyze data using statistical techniques, and communicate the findings effectively to stakeholders. Furthermore, a data scientist should be able to design and develop predictive models using machine learning algorithms to solve real-world problems.
As someone who has been engaging with math, in one form or another, for over a decade, I find it disappointing that there aren't many books that teach the mathematics underneath data science in a comprehensive, short and sharp manner. This book aims at doing exactly that. Really, the need for writing this book is best captured by its title! There is a myriad of books on data science, machine learning and their underlying mathematics, but they're each around 350 pages long, with a high price, which only touch upon a few topics, mainly written in code, with much less actual teaching of mathematics, or doing some but in very advanced levels.
The book has two parts. Part I explores the core mathematics at use in data science. Part II builds upon the knowledge gained in the first part and explores the underlying mathematics of some of the well-known machine learning algorithms and models; it also provides sample codes to implement each of the algorithms in the programming language Python.
147 pages, Paperback
Published May 7, 2023
1 person is currently reading
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
No one has reviewed this book yet.